Can Lung Function Be Used as a Predictor of Cerebral Stroke?

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Walid I.M. Elgendy, Dalia Anas, Rania S. Nageeb, Hanan A. Hassan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4947297/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract objectives Limited studies have explored the specific relationship between pulmonary function, arterial blood gases, and cerebral stroke. This study aims to investigate the relationship between lung function and carotid intima-media thickness (CIMT) in cerebral stroke and non-stroke patients without chronic pulmonary disease. Methods A case control study included 125 cerebral stroke patients and 125 non-stroke controls selected from the outpatient clinics of the Neurology and Rheumatology & Rehabilitation departments at Zagazig University Hospital, Sharkia Governorate, Egypt. Lung function was assessed using the forced vital capacity (FVC) maneuver. Arterial blood gases were measured, and CIMT was evaluated using high-resolution ultrasonography by trained physicians. Results The results revealed a higher risk of cerebral stroke in patients with elevated CIMT compared to those with normal CIMT. An inverse relationship was observed between lung function, expressed as FVC, FEV1/FVC, and MVV, and arterial blood gases, expressed as PaO2, with CIMT. Reduced FVC, FEV1/FVC, MVV, and PaO2 were associated with elevated CIMT. The stepwise multivariable regression model showed that CIMT was directly related to age and FEV1 and inversely related to FVC, FEV1/FVC, PaO2, and MVV. Conclusion Patients with elevated CIMT have a higher risk of cerebral stroke. Reduced FVC, FEV1/FVC, MVV, and PaO2 are associated with elevated CIMT. These findings suggest that lung function tests could be useful in screening individuals without respiratory disease who are at high risk for cerebral stroke. Cerebral stroke Carotid intima-media thickness Lung function Arterial blood gases Introduction The physiological aging of the lungs and the inevitable decline in respiratory muscle strength lead to reductions in forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF). These decreases in lung function are closely associated with impairments in both physical and psychological well-being. Moreover, even subclinical lung function impairments or mild pulmonary diseases can adversely affect cardiovascular function and contribute to cardiovascular diseases ( 1 ). A notable relationship between chronic obstructive pulmonary disease (COPD) and stroke has been established ( 2 ). COPD, characterized by chronic airflow limitation and an exaggerated pulmonary inflammatory response, exhibits a bidirectional relationship with adverse cardiovascular events ( 3 ). Several potential mechanisms underlie this association, including pulmonary hyperinflation, hypoxemia, and pulmonary hypertension, all of which are indicative of declining lung function ( 2 , 4 ). Additionally, decreased lung function and pulmonary hypertension are linked to endothelial dysfunction and elevated levels of inflammatory mediators such as C-reactive protein, interleukin-6, interleukin-8, and tumor necrosis factor-alpha ( 5 , 6 ). These inflammatory markers promote and accelerate atherosclerotic plaque formation, further contributing to cardiovascular events. Despite advances in disease management, stroke remains a leading cause of mortality and morbidity ( 7 ). Stroke patients may experience a reduction of up to 50% in respiratory function compared to age- and gender-matched norms, leading to decreased endurance, dyspnea, increased sedentary behavior, and an elevated risk of recurrent stroke ( 8 ). Carotid intima-media thickness (CIMT) is a sensitive marker of subclinical atherosclerosis, and an increase in CIMT or the presence of plaque on carotid duplex ultrasound may predict ischemic stroke ( 9 ). Research on the association between declining lung function and stroke risk is limited. We hypothesize that reduced lung function could be associated with a higher incidence of stroke, potentially due to shared pathogenic mechanisms involving vessel wall degradation and the destruction of lung parenchyma. This study aimed to investigate the relationship between lung function and carotid intima-media thickness (CIMT) in cerebral stroke and non-stroke patients without chronic pulmonary disease. Methods Patients and study design A case control study included 125 cerebral stroke patients as a patient group, and 125 non-stroke individuals as a control group. The patients selected from the outpatient clinics of the Neurology, Rheumatology, and Rehabilitation departments at Zagazig University Hospital, Sharkia Governorate, Egypt from May 2021 to June 2023. Stroke diagnoses were confirmed by referring neurologists based on brain CT or MRI findings. Patients with a history of malignancy, asthma, chronic lung diseases, or incomplete data collection were excluded. All participants were informed of their rights, privacy, and well-being through consent forms, which they were asked to read and sign if they agreed to participate in the study. Ethics approval, IRB number and consent to participate. This study was conducted in accordance with the ethical principles of the Helsinki Declaration. Ethical approval was granted by the Ethics Committee of Zagazig University Hospital, Faculty of Medicine, Zagazig University (Approval No: 520). Informed consent was obtained from all participants, or from their legal guardians when applicable. This process ensured that participants were fully informed about the study's purpose and nature and that their participation was entirely voluntary. Data collection and measurement The following data was collected from patient’s files and included: Basic demographic data: included age, sex, smoking habits, and other risk factors, along with a thorough clinical examination. Body Mass Index (BMI): Calculated as weight (in kilograms) divided by height (in meters squared). Blood samples for routine laboratory investigations. Pulmonary Function Testing (Spirometry): Lung function was evaluated using computerized spirometry with a Sensor Medics Vmax 229 (Sensor Medics, Yonda Linda, CA, USA) series flow-sensitive spirometer. Measurements included FEV1, FVC, FEV1/FVC ratio, PEF, FEF25-75%, and MVV. Arterial Blood Gases: Assessed for pH, PaO2, PaCO2, HCO3, and oxygen saturation. Carotid Intima-Media Thickness (CIMT) Measurement: CIMT was measured using high-resolution B-mode ultrasonography (LOGIQ F8, GE Healthcare, USA) with a linear 7.5 MHz transducer. Trained physicians performed the measurements on the far wall of the right and left common carotid arteries, 1.5 cm proximal to the bifurcation. The transducer was adjusted to maximize the lumen diameter in the longitudinal plane. CIMT was measured at the end of diastole as the distance from the leading edge of the first echogenic line (lumen-intima interface) to the second echogenic line (media-adventitia interface). The greater value between the right and left common CIMT was used for analysis (10). In healthy middle-aged adults, the CCA lumen-intima interface and the media-adventitia interface typically measure 0.6–0.7 mm (11). Statistical Analysis Data analysis was performed using SPSS version 26. Descriptive characteristics of participants were presented as means and standard deviations (SD) for continuous variables and as numbers and percentages for categorical variables. The independent samples t-test was used to determine significant differences between continuous variables, while the chi-square test was applied to categorical variables. Statistical significance was set at p ≤ 0.05. Odds ratios (OR) and confidence intervals (CI) were calculated to assess the risk of variables associated with cerebrovascular stroke. Multivariable linear regression was conducted on the total sample to examine the association of carotid intima-media thickness (dependent variable) with age, BMI, and lung function parameters (pH, PaO2, PCO2, SpO2, HCO3, FVC, FEV1, FEV1/FVC, PEF, and MVV) as independent variables. Stepwise forward selection in multivariable regression analysis was then used to evaluate the independent association between CIMT and the listed variables. A P value < 0.10 was the criterion for covariates to enter and remain in the regression model. Results In Table 1 , the mean age of stroke patients was 50.4 ± 8.37 years, while the mean age of the non-stroke control group was 44.62 ± 9.78 years. Among the stroke patients, 54.4% were male and 45.6% were female. Similarly, the non-stroke control group comprised 56% males and 44% females, with no statistically significant differences in epidemiological data between the two groups. Regarding smoking status, 24% of stroke patients were non-smokers, 16% were mild smokers, 38.4% were moderate smokers, and 21.6% were heavy smokers. In comparison, the non-stroke control group consisted of 20% non-smokers, 30.4% mild smokers, 34.4% moderate smokers, and 15.2% heavy smokers, with no statistically significant differences between the two groups. There were statistically significant differences between the stroke and non-stroke groups in several parameters. Stroke patients showed a significant decrease in pH, FEV1/FVC ratio, and PEFR, along with a significant increase in PaCO2, HCO3, and CIMT compared to non-stroke controls. Table 1 Characteristics of the studied cerebral stroke and non-stroke patients. Variable Stroke patients. (No = 125) Non stroke control. (No = 125) P value Age (years) 50.40 ± 8.37 44.62 ± 9.78 0.18 Sex : Male Female 68 (54.4%) 57 (45.6%) 70 (56%) 55 (44%) 0.899 Smoking : Non-smoker Mild smoker Moderate smoker Heavy smoker 30 (24%) 20 (16%) 48 (38.4%) 27 (21.6%) 25 (20%) 38 (30.4%) 43 (34.4%) 19 (15.2%) 0.052 Body mass index (Kg/m 2 ) 28.36 ± 3.78 26.64 ± 3.83 0.33 PH 7.37 ± 0.013 7.39 ± 0.02 ˂ 0.001* PaO 2 (mmHg) 85.10 ± 4.61 85.24 ± .4.77 0.99 PaCO 2 (mmHg) 47.48 ± 1.89 44.46 ± 2.84 ˂ 0.001* SPO 2% 89.02 ± 2.25 88.80 ± 2.13 0.39 HCO 3 (mlEq/L) 27.70 ± 1.58 25.51 ± 2.19 ˂ 0.001* CIMT (mm) 1.96 ± 0.66 1.07 ± 0.41 ˂ 0.001* FVC (% of predicted) 88.48 ± 2.14 93.37 ± 2.07 0.45 EFV1(% of predicted) 90.48 ± 2.39 92.20 ± 1.91 0.59 EFV1/FVC 90.04 ± 2.38 94.61 ± 1.40 ˂ 0.001* PEFR (% of predicted) 89.88 ± 1.66 92.06 ± 0.95 ˂ 0.001* MVV (% of predicted) 89.52 ± 2.21 90.60 ± 1.56 0.11 * Significant difference Table 2 presented the adjusted odds ratio (OR) and 95% confidence interval (CI) for the association of cerebrovascular stroke with the studied variables. Among these variables, elevated CIMT was associated with a significantly higher risk of stroke (OR 3.67, CI 1.06–12.44). In contrast, sex, smoking, and other risk factors did not show a significant association with stroke. Table 2 Comparison of clinical parameters and CIMT between stroke and non-stroke controls. Variables Stroke patients No = 125 Non stroke controls No = 125 OR 95% CI P value Sex: Female Male 57 68 55 70 1.00 0.94 Ref. 0.56–1.54 0.799 Smoking: Nonsmoker Mild smoker Moderate smoker Heavy smoker 30 20 48 27 25 38 43 19 1.00 0.44 0.93 1.18 Ref. 0.21–0.94 0.48–1.82 0.54–2.61 0.03 0.83 0.68 Carotid intima-medial thickness: Normal Elevated 14 111 58 67 1,00 6.86 Ref. 3.56–13.25* ˂ 0.001 Other risk factors: No risk factors Diabetes mellitus Hypertension Dyslipidemias 66 17 15 27 76 15 14 20 1.00 1.31 1.23 1.55 Ref. 0.61–2.81 0.55–2.74 0.799–3.02 0.5 0.61 0.91 * Significant difference Table 3 summarized the results of the multiple linear regression analysis, showing the relationships between CIMT and other variables. CIMT was positively correlated with age (coefficient = 0.014, standard error = 0.003) and FEV1 (coefficient = 0.06, standard error = 0.02), and negatively correlated with PaO2 (coefficient = -0.022, standard error = 0.006), FVC (coefficient = -0.086, standard error = 0.019), FEV1/FVC ratio (coefficient = -0.079, standard error = 0.018), and MVV (coefficient = -0.053). This indicates that reduced FVC, FEV1/FVC ratio, MVV, and PaO2 were associated with elevated CIMT. Table 3 Multiple linear regression of CIMT and studied covariates. Variable Coefficient (SE) * (No = 250) P value Constant 10.084 (8.347) 0.22 Age 0.014 (0.003) ˂ 0.001* Body mass index 0.013 (0.007) 0.06 PH 0.032 (1.100) 0.97 PaO 2 − 0.022(0.006) ˂ 0.001* PaCO 2 0.012 (0.015) 0.41 SPO 2 0.004 (0.013) 0.72 HCO 3 0.010 (0.019) 0.59 FVC − 0.086 (0.019) ˂ 0.001* EFV1 0.061 (0.021) ˂ 0.001* EFV1/FVC − 0.079 (0.018) ˂ 0.001* PEFR 0.050 (0.029) 0.08 MVV − 0.053 (0.019) ˂ 0.001* * Coefficients represent change in carotid intima-media thickness for an increase in the value of the continuous predictor variables shown in the table. Table 4 detailed the results of the stepwise multiple regression analysis. The regression model included FEV1/FVC ratio, age, PaO2, FVC, FEV1, and MVV, while other variables such as BMI, pH, PaCO2, SpO2, and PEFR were excluded. In models 4, 5, and 6, CIMT was positively related to age and FEV1 and inversely related to FEV1/FVC ratio, PaO2, FVC, and MVV. Table 4 Stepwise multivariate regression models of studied co-variables with carotid intima-medial thickness. Model Variable Coefficient (SE) * (No = 250) Beta P value 1 Constant 13.687 (1.191) ˂ .001 EFV1/FVC − 0.133 (0.013) -0.553 ˂ .001 2 Constant 10.558 (1.166) ˂ .001 EFV1/FVC -0.110 (0.012) -0.458 ˂ .001 Age 0.022 (0.003) 0.362 ˂ .001 3 Constant 13.534 (1.414) ˂ .001 EFV1/FVC -0.117 (0.012) -0.486 ˂ .001 Age 0.016 (0.003) 0.260 ˂ .001 PaO 2 0.024 (0.007) -0.194 ˂ .001 4 Constant 13.901 (1.398) ˂ .001 EFV1/FVC -0.084 (0.016) -0.348 ˂ .001 Age 0.016 (0.003) 0.270 ˂ .001 PaO 2 -0.022 (0.007) -0.179 ˂ .001 FVC -0.040 (0.014) -0.192 ˂ .001 5 Constant 10.684 (1.536) ˂ .001 EFV1/FVC − 0.080 (0.016) -0.334 ˂ .001 Age 0.014 (0.003) 0.238 ˂ .001 PaO 2 − 0.023 (0.006) -0.185 ˂ .001 FVC − 0.092 (0.018) -0.443 ˂ .001 EFV1 0.086 (0.020) 0.309 ˂ .001 6 Constant 13.804 (2.084) ˂ .001 EFV1/FVC − 0.073 (0.016) − 0.302 ˂ .001 Age 0.014 (0.003) 0.236 ˂ .001 PaO 2 − .024 (0.006) − 0.195 ˂ .001 FVC − 0.088 (0.018) − 0.427 ˂ .001 EFV1 0.076 (0.020) 0.273 ˂ .001 MVV -0.035 (0.016) -0.104 ˂ .001 * Coefficients represent changes in carotid intima medial thickness for an increase in the value of the continuous predictor variables shown in the table. Discussion Reduced lung function has been identified as a significant predictor of cardiovascular mortality. Studies by Wang et al. ( 12 ) and Silvestre et al. ( 13 ) have demonstrated a link between lower pulmonary function and an increased risk of stroke ( 12 , 13 ). This association may be due to alterations in cardiac structure and compensatory mechanisms ( 1 ), pulmonary hypertension ( 3 ), inflammation, and oxidative stress ( 14 ). Additionally, changes in respiratory load and thoracic pressure can lead to significant cardiac stress. Respiratory distress caused by airway constriction can induce substantial hemodynamic changes and impact cardiac structure (1,3). Severe airflow obstruction reduces left ventricular size, leading to decreased stroke volume and cardiac output ( 15 ). These effects may result from impaired ventricular filling, which can occur even in the early stages of obstructive pulmonary diseases due to hyperinflation ( 16 ). Moreover, pulmonary hypertension can increase right atrial pressure, potentially allowing paradoxical emboli to pass through a patent foramen ovale, thus heightening the risk of stroke ( 17 , 18 ). In this study, we explored the relationship between lung function and carotid intima-media thickness (CIMT) in cerebral stroke patients compared to non-stroke controls, all without chronic pulmonary disease. Our findings revealed that the risk of cerebral stroke was significantly higher in patients with elevated CIMT compared to those with normal CIMT (OR 6.86, CI 3.56–13.25). These results align with previous study ( 19 ), in which a significant increase in CIMT among patients with CT-confirmed ischemic stroke was recorded. Similarly, another study found that CIMT was elevated in patients with ischemic stroke ( 9 ). Our study also demonstrated an inverse relationship between lung function, as measured by FVC, FEV1/FVC, and MVV, and CIMT. In the same line with another report that impaired lung function, estimated by FVC (% pred.) and FEV1 (% pred.), was associated with elevated CIMT in a middle-aged population without chronic pulmonary disease ( 10 ). Additionally, Watanabe et al. ( 20 ) reported an inverse correlation between FEV1 and CIMT in both smokers and the general population. We also observed an inverse relationship between PaO2 and CIMT, indicating that lower PaO2 levels were associated with increased CIMT. However, no significant correlations were found between CIMT and other arterial blood gas parameters, including pH, PaCO2, O2 saturation, and HCO3. The results of our stepwise multivariable regression model showed that CIMT was positively associated with age and FEV1, and inversely associated with FEV1/FVC, PaO2, FVC, and MVV in models 4, 5, and 6. That came in consistent with those reductions in FVC, FEV1/FVC%, MVV, and PaO2 were associated with elevated CIMT ( 20 ). The beforementioned study also found that stroke patients had lower FEV1/FVC% ratios compared to non-stroke patients. Takase et al. ( 21 ) similarly found that, after adjusting for passive smoking, lower FEV1 and FVC were linked to higher CIMT, an association that persisted even when the analysis was limited to never-smokers. Moreover, when participants were stratified by age, an inverse relationship between lung function and CIMT was confirmed in both middle-aged and elderly groups. Furthermore, a significant inverse association between the FEV1/FVC ratio z-scores and CIMT after adjusting for covariates ( 22 ). Several studies have explored the relationship between CIMT and ischemic stroke severity ( 23 – 25 ). They found that elevated CIMT values, along with high National Institutes of Health Stroke Scale scores at admission, strongly predict both short-term functional impairment and mortality three months after acute ischemic stroke ( 23 – 25 ). Moreover, Gulsvik et al. ( 26 ) and Drakopanagiotakis et al. ( 27 ) established a link between lung function and fatal strokes. Given our findings of an inverse relationship between lung function and CIMT, further research is warranted to evaluate the association between abnormal pulmonary function tests and stroke severity and subtypes. These tests could be valuable in guiding early treatment strategies to improve post-stroke functional outcomes. However, our study has some limitations. The non-stroke patients with lower respiratory function and PaO2 may require many years of follow-up to determine who develops cerebral stroke, which is necessary to confirm a specific relationship. In conclusion, our study found that the risk of cerebral stroke was higher among patients with elevated CIMT compared to those with normal CIMT. Reduced FVC, FEV1/FVC, MVV, and PaO2 were associated with increased CIMT. These findings highlight the importance of lung function testing as a screening tool for identifying individuals at high risk for cerebral stroke, even in the absence of respiratory disease. Declarations Ethics approval and consent to participate. "Ethical approval has been obtained from ethical committee office, Zagazig University Hospital, Faculty of Medicine, Zagazig University (Approval No: 520) " Conflict of interest “The authors have no conflicts of interest.” Funding "This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors." Author's contribution "W.I.M. E., D.A. and S.L. worked on study design and data collection, R.S.N. and H.A.H. analyzed and interpreted the results. All authors were significant contributors to writing up the manuscript. All authors have read and approved the final manuscript." Acknowledgements "Not applicable" Availability of data and material "The datasets used during the current study available from the corresponding author on reasonable request." Consent for publication. "Not applicable" References Ramalho SHR, Shah AM. Lung function and cardiovascular disease: A link. Trends Cardiovasc Med. 2021 Feb;31(2):93-98. doi: 10.1016/j.tcm.2019.12.009. Epub 2020 Jan 3. PMID: 31932098; PMCID: PMC7332411. Ann Morgan, Kieran Rothnie, Krishnan Bhaskaran, Liam Smeeth, Jennifer Quint. Risk of stroke in COPD: a matched cohort study. European Respiratory Journal 2017 50: PA1569; DOI: 10.1183/1393003.congress-2017.PA1569 Rabe KF, Hurst JR, Suissa S. Cardiovascular disease and COPD: dangerous liaisons? Eur Respir Rev. 2018 Oct 3;27(149):180057. doi: 10.1183/16000617.0057-2018. Erratum in: Eur Respir Rev. 2018 Nov 21;27(150):185057. doi: 10.1183/16000617.5057-2018. PMID: 30282634; PMCID: PMC9488649. Ghoorah K, De Soyza A, Kunadian V. 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Epub 2021 Aug 4. PMID: 34347209. Lee YH, Yeh SJ. Correlation of common carotid artery intima media thickness, intracranial arterial stenosis and post-stroke cognitive impairment. Acta Neurol Taiwan. 2007 Dec;16(4):207-13. PMID: 18220013 Heliopoulos I, Papaoiakim M, Tsivgoulis G, Chatzintounas T, Vadikolias K, Papanas N, Piperidou C. Common carotid intima media thickness as a marker of clinical severity in patients with symptomatic extracranial carotid artery stenosis. Clin Neurol Neurosurg. 2009 Apr;111(3):246-50. doi: 10.1016/j.clineuro.2008.10.007. Epub 2008 Nov 25. PMID: 19036498. Gulsvik AK, Gulsvik A, Skovlund E, Thelle DS, Mowé M, Humerfelt S, Wyller TB. The association between lung function and fatal stroke in a community followed for 4 decades. J Epidemiol Community Health. 2012 Nov;66(11):1030-6. doi: 10.1136/jech-2011-200312. Epub 2012 Apr 7. PMID: 22493479. Drakopanagiotakis F, Bonelis K, Steiropoulos P, Tsiptsios D, Sousanidou A, Christidi F, Gkantzios A, Serdari A, Voutidou S, Takou CM, Kokkotis C, Aggelousis N, Vadikolias K. Pulmonary Function Tests Post-Stroke. Correlation between Lung Function, Severity of Stroke, and Improvement after Respiratory Muscle Training. Neurol Int. 2024 Jan 11;16(1):139-161. doi: 10.3390/neurolint16010009. PMID: 38251057; PMCID: PMC10801624. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Aug, 2024 Editor assigned by journal 22 Aug, 2024 Submission checks completed at journal 21 Aug, 2024 First submitted to journal 20 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4947297","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":343707116,"identity":"fb1e5c35-06fa-4112-856e-1ffb4986eebc","order_by":0,"name":"Walid I.M. Elgendy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYFAC5oYDPAYMcmzMjI0PGBgOEKOFEazFmJ+9+bAB0VoYeBgYEmf2HEuTIEqLwfGDjQfeFGxj3HAjx6yap+aOHD8D88NHN/BpOZPYcHCOwW1mA6CW2zzHnhlLNrAZG+fg0WJ2ILHhMI/BbTaIFrbDiRsO8LBJ49Vy/iFYCw9ISzHPP2K03IDYIiEJ9D4zbxsRWuxvPAT7xQAUyJJz+w4bSzYT8Itkf/LhD2/+3K5vA0blhzffDssB9T58jE8LCmDiAZHMxCoHAcYfpKgeBaNgFIyCEQMA0ypYIOa6VMUAAAAASUVORK5CYII=","orcid":"","institution":"Zagazig University","correspondingAuthor":true,"prefix":"","firstName":"Walid","middleName":"I.M.","lastName":"Elgendy","suffix":""},{"id":343707119,"identity":"238d853a-95da-425e-9e21-b5f86a80ef6d","order_by":1,"name":"Dalia Anas","email":"","orcid":"","institution":"Zagazig University","correspondingAuthor":false,"prefix":"","firstName":"Dalia","middleName":"","lastName":"Anas","suffix":""},{"id":343707122,"identity":"fbcdf721-7171-40b6-8cf3-14a753f6e01f","order_by":2,"name":"Rania S. Nageeb","email":"","orcid":"","institution":"Zagazig University","correspondingAuthor":false,"prefix":"","firstName":"Rania","middleName":"S.","lastName":"Nageeb","suffix":""},{"id":343707131,"identity":"db2eb5c3-1874-454e-bc9f-e3e294028884","order_by":3,"name":"Hanan A. Hassan","email":"","orcid":"","institution":"Zagazig University","correspondingAuthor":false,"prefix":"","firstName":"Hanan","middleName":"A.","lastName":"Hassan","suffix":""},{"id":343707135,"identity":"484c018c-a7cb-4c35-afe1-cb7c4f7d73fd","order_by":4,"name":"Samah Lotfy","email":"","orcid":"","institution":"Zagazig University","correspondingAuthor":false,"prefix":"","firstName":"Samah","middleName":"","lastName":"Lotfy","suffix":""}],"badges":[],"createdAt":"2024-08-20 20:39:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4947297/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4947297/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64834095,"identity":"4058e9f6-b41c-4be8-9f82-ba4fe846ad51","added_by":"auto","created_at":"2024-09-19 10:14:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":611799,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4947297/v1/eacfb7ee-e38c-4f42-902c-37adda4b31ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Can Lung Function Be Used as a Predictor of Cerebral Stroke?","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe physiological aging of the lungs and the inevitable decline in respiratory muscle strength lead to reductions in forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF). These decreases in lung function are closely associated with impairments in both physical and psychological well-being. Moreover, even subclinical lung function impairments or mild pulmonary diseases can adversely affect cardiovascular function and contribute to cardiovascular diseases (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). A notable relationship between chronic obstructive pulmonary disease (COPD) and stroke has been established (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCOPD, characterized by chronic airflow limitation and an exaggerated pulmonary inflammatory response, exhibits a bidirectional relationship with adverse cardiovascular events (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Several potential mechanisms underlie this association, including pulmonary hyperinflation, hypoxemia, and pulmonary hypertension, all of which are indicative of declining lung function (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Additionally, decreased lung function and pulmonary hypertension are linked to endothelial dysfunction and elevated levels of inflammatory mediators such as C-reactive protein, interleukin-6, interleukin-8, and tumor necrosis factor-alpha (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These inflammatory markers promote and accelerate atherosclerotic plaque formation, further contributing to cardiovascular events.\u003c/p\u003e \u003cp\u003eDespite advances in disease management, stroke remains a leading cause of mortality and morbidity (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Stroke patients may experience a reduction of up to 50% in respiratory function compared to age- and gender-matched norms, leading to decreased endurance, dyspnea, increased sedentary behavior, and an elevated risk of recurrent stroke (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCarotid intima-media thickness (CIMT) is a sensitive marker of subclinical atherosclerosis, and an increase in CIMT or the presence of plaque on carotid duplex ultrasound may predict ischemic stroke (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Research on the association between declining lung function and stroke risk is limited. We hypothesize that reduced lung function could be associated with a higher incidence of stroke, potentially due to shared pathogenic mechanisms involving vessel wall degradation and the destruction of lung parenchyma. This study aimed to investigate the relationship between lung function and carotid intima-media thickness (CIMT) in cerebral stroke and non-stroke patients without chronic pulmonary disease.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients and study design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA case control study included 125 cerebral stroke patients as a patient group, and 125 non-stroke individuals as a control group. The patients selected from the outpatient clinics of the Neurology, Rheumatology, and Rehabilitation departments at Zagazig University Hospital, Sharkia Governorate, Egypt from May 2021 to June 2023. Stroke diagnoses were confirmed by referring neurologists based on brain CT or MRI findings. Patients with a history of malignancy, asthma, chronic lung diseases, or incomplete data collection were excluded. All participants were informed of their rights, privacy, and well-being through consent forms, which they were asked to read and sign if they agreed to participate in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval, IRB number and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical principles of the Helsinki Declaration. Ethical approval was granted by the Ethics Committee of Zagazig University Hospital, Faculty of Medicine, Zagazig University (Approval No: 520). Informed consent was obtained from all participants, or from their legal guardians when applicable. This process ensured that participants were fully informed about the study\u0026apos;s purpose and nature and that their participation was entirely voluntary.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following data was collected from patient\u0026rsquo;s files and included:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eBasic demographic data: included\u0026nbsp;age, sex, smoking habits, and other risk factors, along with a thorough clinical examination.\u003c/li\u003e\n \u003cli\u003eBody Mass Index (BMI): Calculated as weight (in kilograms) divided by height (in meters squared).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBlood samples for routine laboratory investigations.\u003c/li\u003e\n \u003cli\u003ePulmonary Function Testing (Spirometry): Lung function was evaluated using computerized spirometry with a Sensor Medics Vmax 229 (Sensor Medics, Yonda Linda, CA, USA) series flow-sensitive spirometer. Measurements included FEV1, FVC, FEV1/FVC ratio, PEF, FEF25-75%, and MVV.\u003c/li\u003e\n \u003cli\u003eArterial Blood Gases: Assessed for pH, PaO2, PaCO2, HCO3, and oxygen saturation.\u003c/li\u003e\n \u003cli\u003eCarotid Intima-Media Thickness (CIMT) Measurement: CIMT was measured using high-resolution B-mode ultrasonography (LOGIQ F8, GE Healthcare, USA) with a linear 7.5 MHz transducer. Trained physicians performed the measurements on the far wall of the right and left common carotid arteries, 1.5 cm proximal to the bifurcation. The transducer was adjusted to maximize the lumen diameter in the longitudinal plane. CIMT was measured at the end of diastole as the distance from the leading edge of the first echogenic line (lumen-intima interface) to the second echogenic line (media-adventitia interface). The greater value between the right and left common CIMT was used for analysis (10). In healthy middle-aged adults, the CCA lumen-intima interface and the media-adventitia interface typically measure 0.6\u0026ndash;0.7 mm (11).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis was performed using SPSS version 26. Descriptive characteristics of participants were presented as means and standard deviations (SD) for continuous variables and as numbers and percentages for categorical variables. The independent samples t-test was used to determine significant differences between continuous variables, while the chi-square test was applied to categorical variables. Statistical significance was set at p \u0026le; 0.05. Odds ratios (OR) and confidence intervals (CI) were calculated to assess the risk of variables associated with cerebrovascular stroke. Multivariable linear regression was conducted on the total sample to examine the association of carotid intima-media thickness (dependent variable) with age, BMI, and lung function parameters (pH, PaO2, PCO2, SpO2, HCO3, FVC, FEV1, FEV1/FVC, PEF, and MVV) as independent variables. Stepwise forward selection in multivariable regression analysis was then used to evaluate the independent association between CIMT and the listed variables. A P value \u0026lt; 0.10 was the criterion for covariates to enter and remain in the regression model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the mean age of stroke patients was 50.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37 years, while the mean age of the non-stroke control group was 44.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.78 years. Among the stroke patients, 54.4% were male and 45.6% were female. Similarly, the non-stroke control group comprised 56% males and 44% females, with no statistically significant differences in epidemiological data between the two groups. Regarding smoking status, 24% of stroke patients were non-smokers, 16% were mild smokers, 38.4% were moderate smokers, and 21.6% were heavy smokers. In comparison, the non-stroke control group consisted of 20% non-smokers, 30.4% mild smokers, 34.4% moderate smokers, and 15.2% heavy smokers, with no statistically significant differences between the two groups. There were statistically significant differences between the stroke and non-stroke groups in several parameters. Stroke patients showed a significant decrease in pH, FEV1/FVC ratio, and PEFR, along with a significant increase in PaCO2, HCO3, and CIMT compared to non-stroke controls.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the studied cerebral stroke and non-stroke patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStroke patients.\u003c/p\u003e \u003cp\u003e(No\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon stroke control.\u003c/p\u003e \u003cp\u003e(No\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e50.40\u0026thinsp;\u0026plusmn;\u0026thinsp;8.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.62\u0026thinsp;\u0026plusmn;\u0026thinsp;9.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68 (54.4%)\u003c/p\u003e \u003cp\u003e57 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70 (56%)\u003c/p\u003e \u003cp\u003e55 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eNon-smoker\u003c/p\u003e \u003cp\u003eMild smoker\u003c/p\u003e \u003cp\u003eModerate smoker\u003c/p\u003e \u003cp\u003eHeavy smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (24%)\u003c/p\u003e \u003cp\u003e20 (16%)\u003c/p\u003e \u003cp\u003e48 (38.4%)\u003c/p\u003e \u003cp\u003e27 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (20%)\u003c/p\u003e \u003cp\u003e38 (30.4%)\u003c/p\u003e \u003cp\u003e43 (34.4%)\u003c/p\u003e \u003cp\u003e19 (15.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index (Kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.36\u0026thinsp;\u0026plusmn;\u0026thinsp;3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.64\u0026thinsp;\u0026plusmn;\u0026thinsp;3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2 (mmHg)\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e85.10\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e85.24\u0026thinsp;\u0026plusmn;\u0026thinsp;.4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2 (mmHg)\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e47.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e44.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2%\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e89.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e88.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3 (mlEq/L)\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e27.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e25.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCIMT (mm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC (% of predicted)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e88.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e93.37\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEFV1(% of predicted)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e90.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e92.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEFV1/FVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e90.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e94.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePEFR (% of predicted)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e89.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e92.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMVV (% of predicted)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e89.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e90.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Significant difference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the adjusted odds ratio (OR) and 95% confidence interval (CI) for the association of cerebrovascular stroke with the studied variables. Among these variables, elevated CIMT was associated with a significantly higher risk of stroke (OR 3.67, CI 1.06\u0026ndash;12.44). In contrast, sex, smoking, and other risk factors did not show a significant association with stroke.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical parameters and CIMT between stroke and non-stroke controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStroke patients No\u0026thinsp;=\u0026thinsp;125\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon stroke controls No\u0026thinsp;=\u0026thinsp;125\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex:\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRef.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.56\u0026ndash;1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking:\u003c/p\u003e \u003cp\u003eNonsmoker\u003c/p\u003e \u003cp\u003eMild smoker\u003c/p\u003e \u003cp\u003eModerate smoker\u003c/p\u003e \u003cp\u003eHeavy smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003cp\u003e48\u003c/p\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003cp\u003e38\u003c/p\u003e \u003cp\u003e43\u003c/p\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003cp\u003e0.44\u003c/p\u003e \u003cp\u003e0.93\u003c/p\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRef.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.21\u0026ndash;0.94\u003c/p\u003e \u003cp\u003e0.48\u0026ndash;1.82\u003c/p\u003e \u003cp\u003e0.54\u0026ndash;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e0.83\u003c/p\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotid intima-medial thickness:\u003c/p\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003eElevated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRef.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e3.56\u0026ndash;13.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e˂\u003c/b\u003e 0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther risk factors:\u003c/p\u003e \u003cp\u003eNo risk factors\u003c/p\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003cp\u003eDyslipidemias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66\u003c/p\u003e \u003cp\u003e17\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003cp\u003e15\u003c/p\u003e \u003cp\u003e14\u003c/p\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.00\u003c/b\u003e\u003c/p\u003e \u003cp\u003e1.31\u003c/p\u003e \u003cp\u003e1.23\u003c/p\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eRef.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e0.61\u0026ndash;2.81\u003c/p\u003e \u003cp\u003e0.55\u0026ndash;2.74\u003c/p\u003e \u003cp\u003e0.799\u0026ndash;3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003cp\u003e0.61\u003c/p\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e* Significant difference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarized the results of the multiple linear regression analysis, showing the relationships between CIMT and other variables. CIMT was positively correlated with age (coefficient\u0026thinsp;=\u0026thinsp;0.014, standard error\u0026thinsp;=\u0026thinsp;0.003) and FEV1 (coefficient\u0026thinsp;=\u0026thinsp;0.06, standard error\u0026thinsp;=\u0026thinsp;0.02), and negatively correlated with PaO2 (coefficient = -0.022, standard error\u0026thinsp;=\u0026thinsp;0.006), FVC (coefficient = -0.086, standard error\u0026thinsp;=\u0026thinsp;0.019), FEV1/FVC ratio (coefficient = -0.079, standard error\u0026thinsp;=\u0026thinsp;0.018), and MVV (coefficient = -0.053). This indicates that reduced FVC, FEV1/FVC ratio, MVV, and PaO2 were associated with elevated CIMT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression of CIMT and studied covariates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (SE) *\u003c/p\u003e \u003cp\u003e(No\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.084 (8.347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.014 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBody mass index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.013 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.032 (1.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.022(0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePaCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012 (0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSPO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004 (0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHCO\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.010 (0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.086 (0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEFV1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.061 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEFV1/FVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.079 (0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePEFR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050 (0.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMVV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.053 (0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* Coefficients represent change in carotid intima-media thickness for an increase in the value of the continuous predictor variables shown in the table.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e detailed the results of the stepwise multiple regression analysis. The regression model included FEV1/FVC ratio, age, PaO2, FVC, FEV1, and MVV, while other variables such as BMI, pH, PaCO2, SpO2, and PEFR were excluded. In models 4, 5, and 6, CIMT was positively related to age and FEV1 and inversely related to FEV1/FVC ratio, PaO2, FVC, and MVV.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStepwise multivariate regression models of studied co-variables with carotid intima-medial thickness.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient (SE) *\u003c/p\u003e \u003cp\u003e(No\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.687 (1.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEFV1/FVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.133 (0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.558 (1.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEFV1/FVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.110 (0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.022 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.534 (1.414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEFV1/FVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.117 (0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePaO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.901 (1.398)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEFV1/FVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.084 (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePaO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.022 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.040 (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.684 (1.536)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEFV1/FVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.080 (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePaO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.023 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.092 (0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEFV1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086 (0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.804 (2.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEFV1/FVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.073 (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014 (0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePaO\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.024 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFVC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.088 (0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEFV1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.076 (0.020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMVV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.035 (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e˂ .001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Coefficients represent changes in carotid intima medial thickness for an increase in the value of the continuous predictor variables shown in the table.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eReduced lung function has been identified as a significant predictor of cardiovascular mortality. Studies by Wang et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and Silvestre et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) have demonstrated a link between lower pulmonary function and an increased risk of stroke (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This association may be due to alterations in cardiac structure and compensatory mechanisms (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), pulmonary hypertension (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), inflammation, and oxidative stress (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Additionally, changes in respiratory load and thoracic pressure can lead to significant cardiac stress. Respiratory distress caused by airway constriction can induce substantial hemodynamic changes and impact cardiac structure (1,3). Severe airflow obstruction reduces left ventricular size, leading to decreased stroke volume and cardiac output (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These effects may result from impaired ventricular filling, which can occur even in the early stages of obstructive pulmonary diseases due to hyperinflation (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Moreover, pulmonary hypertension can increase right atrial pressure, potentially allowing paradoxical emboli to pass through a patent foramen ovale, thus heightening the risk of stroke (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we explored the relationship between lung function and carotid intima-media thickness (CIMT) in cerebral stroke patients compared to non-stroke controls, all without chronic pulmonary disease. Our findings revealed that the risk of cerebral stroke was significantly higher in patients with elevated CIMT compared to those with normal CIMT (OR 6.86, CI 3.56\u0026ndash;13.25). These results align with previous study (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), in which a significant increase in CIMT among patients with CT-confirmed ischemic stroke was recorded. Similarly, another study found that CIMT was elevated in patients with ischemic stroke (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Our study also demonstrated an inverse relationship between lung function, as measured by FVC, FEV1/FVC, and MVV, and CIMT. In the same line with another report that impaired lung function, estimated by FVC (% pred.) and FEV1 (% pred.), was associated with elevated CIMT in a middle-aged population without chronic pulmonary disease (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, Watanabe et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) reported an inverse correlation between FEV1 and CIMT in both smokers and the general population.\u003c/p\u003e \u003cp\u003eWe also observed an inverse relationship between PaO2 and CIMT, indicating that lower PaO2 levels were associated with increased CIMT. However, no significant correlations were found between CIMT and other arterial blood gas parameters, including pH, PaCO2, O2 saturation, and HCO3. The results of our stepwise multivariable regression model showed that CIMT was positively associated with age and FEV1, and inversely associated with FEV1/FVC, PaO2, FVC, and MVV in models 4, 5, and 6. That came in consistent with those reductions in FVC, FEV1/FVC%, MVV, and PaO2 were associated with elevated CIMT (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The beforementioned study also found that stroke patients had lower FEV1/FVC% ratios compared to non-stroke patients. Takase et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) similarly found that, after adjusting for passive smoking, lower FEV1 and FVC were linked to higher CIMT, an association that persisted even when the analysis was limited to never-smokers. Moreover, when participants were stratified by age, an inverse relationship between lung function and CIMT was confirmed in both middle-aged and elderly groups. Furthermore, a significant inverse association between the FEV1/FVC ratio z-scores and CIMT after adjusting for covariates (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have explored the relationship between CIMT and ischemic stroke severity (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). They found that elevated CIMT values, along with high National Institutes of Health Stroke Scale scores at admission, strongly predict both short-term functional impairment and mortality three months after acute ischemic stroke (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Moreover, Gulsvik et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and Drakopanagiotakis et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) established a link between lung function and fatal strokes. Given our findings of an inverse relationship between lung function and CIMT, further research is warranted to evaluate the association between abnormal pulmonary function tests and stroke severity and subtypes. These tests could be valuable in guiding early treatment strategies to improve post-stroke functional outcomes.\u003c/p\u003e \u003cp\u003eHowever, our study has some limitations. The non-stroke patients with lower respiratory function and PaO2 may require many years of follow-up to determine who develops cerebral stroke, which is necessary to confirm a specific relationship.\u003c/p\u003e \u003cp\u003eIn conclusion, our study found that the risk of cerebral stroke was higher among patients with elevated CIMT compared to those with normal CIMT. Reduced FVC, FEV1/FVC, MVV, and PaO2 were associated with increased CIMT. These findings highlight the importance of lung function testing as a screening tool for identifying individuals at high risk for cerebral stroke, even in the absence of respiratory disease.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;Ethical approval has been obtained from ethical committee office, Zagazig University Hospital, Faculty of Medicine, Zagazig University (Approval No: 520) \u0026quot;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;The authors have no conflicts of interest.\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor\u0026apos;s contribution\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;W.I.M. E., D.A. and S.L. worked on study design and data collection, R.S.N. and H.A.H. analyzed and interpreted the results. All authors were significant contributors to writing up the manuscript. All authors have read and approved the final manuscript.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;Not applicable\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;The datasets used during the current study available from the corresponding author on reasonable request.\u0026quot;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026quot;Not applicable\u0026quot;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRamalho SHR, Shah AM. Lung function and cardiovascular disease: A link. 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A Novel Factor in Determining the Risk of Ischemic Cerebrovascular Events in Patients with Atrial Fibrillation: Pulmonary Hypertension. J Stroke Cerebrovasc Dis. 2022 Apr;31(4):106387. doi: 10.1016/j.jstrokecerebrovasdis.2022.106387. Epub 2022 Feb 16. PMID: 35182946.\u003c/li\u003e\n \u003cli\u003eMachida A, Soejima I, Bo T, Amano E, Ota K, Kanno Y, Kakuta T. Paradoxical Cerebral Embolism as Initial Manifestation of Chronic Thromboembolic Pulmonary Hypertension: A Case Report. J Stroke Cerebrovasc Dis. 2019 Sep;28(9):e135-e138. doi: 10.1016/j.jstrokecerebrovasdis.2019.06.006. Epub 2019 Jun 26. PMID: 31253482.\u003c/li\u003e\n \u003cli\u003eJain J, Lathia T, Gupta OP, Jain V. Carotid intima-media thickness and apolipoproteins in patients of ischemic stroke in a rural hospital setting in central India: A cross-sectional study. J Neurosci Rural Pract. 2012 Jan;3(1):21-7. doi: 10.4103/0976-3147.91926. 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Association between impaired lung function and carotid intima-media thickness in children. ERJ Open Res. 2023 Oct 30;9(5):00330-2023. doi: 10.1183/23120541.00330-2023. PMID: 37908396; PMCID: PMC10613963.\u003c/li\u003e\n \u003cli\u003eLehmann ALCF, Alfieri DF, de Ara\u0026uacute;jo MCM, Trevisani ER, Nagao MR, Pesente FS, Gelinski JR, de Freitas LB, Flauzino T, Lehmann MF, Lozovoy MAB, Bregan\u0026oacute; JW, Sim\u0026atilde;o ANC, Maes M, Reiche EMV. Carotid intima media thickness measurements coupled with stroke severity strongly predict short-term outcome in patients with acute ischemic stroke: a machine learning study. Metab Brain Dis. 2021 Oct;36(7):1747-1761. doi: 10.1007/s11011-021-00784-7. Epub 2021 Aug 4. PMID: 34347209.\u003c/li\u003e\n \u003cli\u003eLee YH, Yeh SJ. Correlation of common carotid artery intima media thickness, intracranial arterial stenosis and post-stroke cognitive impairment. Acta Neurol Taiwan. 2007 Dec;16(4):207-13. PMID: 18220013\u003c/li\u003e\n \u003cli\u003eHeliopoulos I, Papaoiakim M, Tsivgoulis G, Chatzintounas T, Vadikolias K, Papanas N, Piperidou C. Common carotid intima media thickness as a marker of clinical severity in patients with symptomatic extracranial carotid artery stenosis.\u0026nbsp;Clin Neurol Neurosurg. 2009 Apr;111(3):246-50. doi: 10.1016/j.clineuro.2008.10.007. Epub 2008 Nov 25. PMID: 19036498.\u003c/li\u003e\n \u003cli\u003eGulsvik AK, Gulsvik A, Skovlund E, Thelle DS, Mow\u0026eacute; M, Humerfelt S, Wyller TB. The association between lung function and fatal stroke in a community followed for 4 decades. J Epidemiol Community Health. 2012 Nov;66(11):1030-6. doi: 10.1136/jech-2011-200312. Epub 2012 Apr 7. PMID: 22493479.\u003c/li\u003e\n \u003cli\u003eDrakopanagiotakis F, Bonelis K, Steiropoulos P, Tsiptsios D, Sousanidou A, Christidi F, Gkantzios A, Serdari A, Voutidou S, Takou CM, Kokkotis C, Aggelousis N, Vadikolias K. Pulmonary Function Tests Post-Stroke. 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PMID: 38251057; PMCID: PMC10801624.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-bronchology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Bronchology](https://ejb.springeropen.com/)","snPcode":"43168","submissionUrl":"https://submission.nature.com/new-submission/43168/3","title":"The Egyptian Journal of Bronchology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cerebral stroke, Carotid intima-media thickness, Lung function, Arterial blood gases","lastPublishedDoi":"10.21203/rs.3.rs-4947297/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4947297/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eobjectives\u003c/h2\u003e \u003cp\u003eLimited studies have explored the specific relationship between pulmonary function, arterial blood gases, and cerebral stroke. This study aims to investigate the relationship between lung function and carotid intima-media thickness (CIMT) in cerebral stroke and non-stroke patients without chronic pulmonary disease.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA case control study included 125 cerebral stroke patients and 125 non-stroke controls selected from the outpatient clinics of the Neurology and Rheumatology \u0026amp; Rehabilitation departments at Zagazig University Hospital, Sharkia Governorate, Egypt. Lung function was assessed using the forced vital capacity (FVC) maneuver. Arterial blood gases were measured, and CIMT was evaluated using high-resolution ultrasonography by trained physicians.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results revealed a higher risk of cerebral stroke in patients with elevated CIMT compared to those with normal CIMT. An inverse relationship was observed between lung function, expressed as FVC, FEV1/FVC, and MVV, and arterial blood gases, expressed as PaO2, with CIMT. Reduced FVC, FEV1/FVC, MVV, and PaO2 were associated with elevated CIMT. The stepwise multivariable regression model showed that CIMT was directly related to age and FEV1 and inversely related to FVC, FEV1/FVC, PaO2, and MVV.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePatients with elevated CIMT have a higher risk of cerebral stroke. Reduced FVC, FEV1/FVC, MVV, and PaO2 are associated with elevated CIMT. These findings suggest that lung function tests could be useful in screening individuals without respiratory disease who are at high risk for cerebral stroke.\u003c/p\u003e","manuscriptTitle":"Can Lung Function Be Used as a Predictor of Cerebral Stroke?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-19 10:06:43","doi":"10.21203/rs.3.rs-4947297/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-22T12:46:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-22T06:45:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-21T05:41:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Bronchology","date":"2024-08-20T20:37:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-bronchology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Bronchology](https://ejb.springeropen.com/)","snPcode":"43168","submissionUrl":"https://submission.nature.com/new-submission/43168/3","title":"The Egyptian Journal of Bronchology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6eabe4a3-5f19-4925-b146-cd924bceb38a","owner":[],"postedDate":"September 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-12-06T05:23:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-19 10:06:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4947297","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4947297","identity":"rs-4947297","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}